Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy
Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy
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DOI：
10.1021/acs.analchem.8b01128
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发表时间：
2018-05-15
影响因子：
7.4
通讯作者：
Tao, Nongjian
中科院分区：
文献类型：
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作者：
Yu, Hui;Jing, Wenwen;Tao, Nongjian
及时确定细菌感染的抗菌药物敏感性可实现精确处方，缩短治疗时间，并有助于最大限度地减少抗生素耐药性感染的传播。目前的抗菌药物敏感性测试（AST）方法通常需要几天时间，因此阻碍了这些临床和健康益处。在这里，我们提出了一种AST方法，通过真实的实时成像尿液中自由移动的细菌细胞，并用深度学习算法分析视频。深度学习算法通过学习细胞的多个表型特征来确定抗生素是否抑制细菌细胞，而无需定义和量化每个特征。我们将该方法应用于尿路感染，一种影响数百万人的常见感染，以确定30分钟内不同抗生素的细菌加标尿液和临床感染尿液样本中病原体的最低抑菌浓度，并使用金标准肉汤大量稀释法验证结果。基于深度学习视频显微镜的AST具有很大的潜力，可以为解决日益增加的耐药感染做出贡献。
Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clinical and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm determines if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to determine the minimum inhibitory concentration of pathogens from both bacteria spiked urine and clinical infected urine samples for different antibiotics within 30 min and validate the results with the gold standard broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the solution of increasing drug-resistant infections.